Parameter Optimization of a Viral Dynamics Model in the Mucus Layer of the Human Nasal Cavity-Nasopharynx Based on Computational Fluid-Particle and Host-Cell Dynamics

Abstract

Respiratory diseases, such as COVID-19 (coronavirus disease 2019) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), have posed a threat to human health. For infection control and a better understanding of the pathogenesis, this study mainly focused on elucidating the virus dynamics in the mucus layer of the human nasal cavity-nasopharynx, using coupled computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) analyses. To reproduce virus transportation in the mucus layer by mucociliary motion, a three-dimensional-shell model was created using the data obtained from computed tomography (CT) of the human upper airway. By considering the mucus milieu, the target-cell-limited model was coupled with the convection-diffusion term to develop the HCD model. Parameter optimization has been shown to have a great impact on the accuracy of model prediction; therefore, this study proposes a method that divides the geometric model into multiple regions and uses Monolix for nonlinear mixed effects modeling for pharmacometrics. The results showed that data from human inoculation challenge trials could be used to estimate the corresponding parameters. The models developed and used with optimized parameters can provide relatively accurate predictions of virus dynamics, which could contribute to the prevention and treatment of respiratory diseases.